arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 595 records · Page 33Linked to original sources

arXiv2021

Mesoscopic Transport of Quantum Anomalous Hall Effect in Sub-Micron Size Regime

The quantum anomalous Hall (QAH) effect has been demonstrated in two-dimensional topological insulator systems incorporated with ferromagnetism. However, a comprehensive understanding of mesoscopic transport in sub-micron QAH devices has yet been established. Here we fabricated miniaturized QAH devices with channel widths down to 600 nm, where the QAH features are still preserved. A back-scattering channel is formed in narrow QAH devices through percolative hopping between 2D compressible puddles. Large resistance fluctuations are observed in narrow devices near the coercive field, which is associated with collective interference between intersecting paths along domain walls when the device geometry is smaller than the phase coherence length $L_ϕ$. Through measurement of size-dependent breakdown current, we confirmed that the chiral edge states are confined at the physical boundary with its width on the order of Fermi wavelength.

cond-mat.mes-hall↗

arXiv2021

Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT

Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited. Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients. Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from "Lung Nodule Analysis 2016"). The synthetic images and masks were used to train a 2D U-net neural network and tested on 203 COVID-19 datasets to generate lung and lesion segmentations. Disease severity scores (DL: damage load; DS: damage score) were calculated based on the segmentations. Correlations between DL/DS and clinical lab tests were evaluated using Pearson's method. A p-value < 0.05 was considered as statistical significant. Results: Automatic lung and lesion segmentations were compared with manual annotations. For lung segmentation, the median values of dice similarity coefficient, Jaccard index and average surface distance, were 98.56%, 97.15% and 0.49 mm, respectively. The same metrics for lesion segmentation were 76.95%, 62.54% and 2.36 mm, respectively. Significant (p << 0.05) correlations were found between DL/DS and percentage lymphocytes tests, with r-values of -0.561 and -0.501, respectively. Conclusion: An AI system that based on thoracic radiographic and data augmentation was proposed to segment lung and lesions in COVID-19 patients. Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.

eess.IV↗

arXiv2021

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

arXiv2021

The design and implementation of GECAM satellite payload performance monitoring software

Background The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is primarily designed to spot gamma-ray bursts corresponding to gravitational waves. In order to achieve stable observations from various astronomical phenomena, the payload performance need to be monitored during the in-orbit operation. Method This article describes the design and implementation of GECAM satellite payload performance monitoring (GPPM) software. The software extracts the payload status and telescope observations (light curves, energy spectrums, characteristic peak fitting of energy spectrums, etc) from the payload data. Considering the large amount of payload status parameters in the engineering data, we have designed a method of parameter processing based on the configuration tables. This method can deal with the frequent changes of the data formats and facilitate program maintenance. Payload status and performance are monitored through defined thresholds and monitoring reports. The entire software is implemented in python language and the huge amount of observation data is stored in MongoDB. Conclusion The design and implementation of GPPM software have been completed, tested with ground and in-orbit payload data. The software can monitor the performance of GECAM payload effectively. The overall design of the software and the data processing method can be applied to other satellites.

astro-ph.IM↗

arXiv2021

Light yield and field dependence measurement in PandaX-II dual-phase xenon detector

The dual-phase xenon time projection chamber (TPC) is one of the most sensitive detector technology for dark matter direct search, where the energy deposition of incoming particle can be converted into photons and electrons through xenon excitation and ionization. The detector response to signal energy deposition varies significantly with the electric field in liquid xenon. We study the detector's light yield and its dependence on the electric field in the PandaX-II dual-phase detector containing 580~kg liquid xenon in the sensitive volume. From our measurements, the light yield at electric fields from 0~V/cm to 317~V/cm is obtained for energy depositions up to 236~keV.

physics.ins-det↗

arXiv2021

Residual Matrix Product State for Machine Learning

Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable accuracy gap between tensor network and the sophisticated neural network models for classical machine learning. In this work, we combine the ideas of matrix product state (MPS), the simplest tensor network structure, and residual neural network and propose the residual matrix product state (ResMPS). The ResMPS can be treated as a network where its layers map the "hidden" features to the outputs (e.g., classifications), and the variational parameters of the layers are the functions of the features of the samples (e.g., pixels of images). This is different from neural network, where the layers map feed-forwardly the features to the output. The ResMPS can equip with the non-linear activations and dropout layers, and outperforms the state-of-the-art tensor network models in terms of efficiency, stability, and expression power. Besides, ResMPS is interpretable from the perspective of polynomial expansion, where the factorization and exponential machines naturally emerge. Our work contributes to connecting and hybridizing neural and tensor networks, which is crucial to further enhance our understand of the working mechanisms and improve the performance of both models.

cs.LG↗

arXiv2021

Improved Efficiency of Open Quantum System Simulations Using Matrix Products States in the Interaction Picture

Modeling open quantum systems -- quantum systems coupled to a bath -- is of value in condensed matter theory, cavity quantum electrodynamics, nanosciences and biophysics. The real-time simulation of open quantum systems was advanced significantly by the recent development of chain mapping techniques and the use of matrix product states that exploit the intrinsic entanglement structure in open quantum systems. The computational cost of simulating open quantum systems, however, remains high when the bath is excited to high-lying quantum states. We develop an approach to reduce the computational costs in such cases. The interaction representation for the open quantum system is used to distribute excitations among the bath degrees of freedom so that the occupation of each bath oscillator is ensured to be low. The interaction picture also causes the matrix dimensions to be much smaller in a matrix product state of a chain-mapped open quantum system than in the Schrödinger picture. Using the interaction representation accelerates the calculations by 1-2 orders of magnitude over existing matrix-product-state method. In the regime of strong system-bath coupling and high temperatures, the speedup can be as large as 3 orders of magnitude. The approach developed here is especially promising to simulate the dynamics of open quantum systems in the high-temperature and strong-coupling regimes.

quant-ph↗

arXiv2021

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.

cs.CV↗

arXiv2021

Selective observation of surface and bulk bands in polar WTe2 by laser-based spin- and angle-resolved photoemission spectroscopy

The electronic state of WTe2, a candidate of type-II Weyl semimetal, is investigated by using laser-based spin- and angle-resolved photoemission spectroscopy (SARPES). We prepare the pair of WTe2 samples, one with (001) surface and the other with (00-1) surface, by "sandwich method", and measure the band structures of each surface separately. The Fermi arcs are observed on both surfaces. We identify that the Fermi arcs on the two surfaces are both originating from surface states. We further find a surface resonance band, which connects with the Fermi-arc band, forming a Dirac-cone-like band dispersion. Our results indicate that the bulk electron and hole bands are much closer in momentum space than band calculations.

cond-mat.mtrl-sci↗

arXiv2021

On the Formation of Hydrogen Peroxide in Water Microdroplets

Recent reports on the formation of hydrogen peroxide (H$_2$O$_2$) in water microdroplets produced via pneumatic spraying or capillary condensation have garnered significant attention. How covalent bonds in water could break under such conditions challenges our textbook understanding of physical chemistry and the water substance. While there is no definitive answer, it has been speculated that ultrahigh electric fields at the air-water interface are responsible for this chemical transformation. Here, we resolve this mystery via a comprehensive experimental investigation of H$_2$O$_2$ formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. Specifically, we assessed the contributions of the evaporative concentration and shock waves in sprays and the effects of trace O$_3$(g) on the H$_2$O$_2$ formation. Glovebox experiments revealed that the H$_2$O$_2$ formation in water microdroplets was most sensitive to the air-borne ozone (O$_3$) concentration. In the absence of O$_3$(g), we could not detect H$_2$O$_2$(aq) in sprays or condensates (detection limit: 250 nM. In contrast, microdroplets exposed to atmospherically relevant O$_3$(g) concentration (10-100 ppb) formed 2-30 $μ$M H$_2$O$_2$(aq); increasing the gas-liquid surface area, mixing, and contact duration increased H$_2$O$_2$(aq) concentration. Thus, the mystery is resolved - the water surface facilitates the O$_3$(g) mass transfer, which is followed by the chemical transformation of O$_3$(aq) into H$_2$O$_2$(aq). These findings should also help us understand the implications of this chemistry in natural and applied contexts.

cond-mat.soft↗

arXiv2021

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above,we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at: https://github.com/hsiangyuzhao/PANet.

eess.IV↗

arXiv2021

Spin-affected reflexive and stretching separation of off-center droplet collision

Recent studies have demonstrated the significant roles of droplet self-spin motion in affecting the head-on collision of binary droplets. In this paper, we present a computational study by using the Volume-of-Fluid (VOF) method to investigate the spin-affected droplet separation of off-center collisions, which are more probable in reality and phenomenologically richer than head-on collisions. Different separation modes are identified through a parametric study with varying spinning speed and impact parameter. A prominent finding is that increasing the droplet spinning speed tends to suppress the reflexive separation and to promote the stretching separation. Physically, the reflexive separation is suppressed because the increased rotational energy reduces the excessive reflexive kinetic energy within the droplet, which is the cause for the droplet reflexive separation. The stretching separation is promoted because the increased droplet angular momentum enhances the local stretching flow within the droplet, which tends to separate the droplet. The roles of orbital angular momentum and spin angular momentum in affecting the droplet separation are further substantiated by studying the collision between two spinning droplets with either the same or opposite chirality. In addition, a theoretical model based on conservation laws is proposed to qualitatively describe the boundaries of coalescence-separation transition influenced by droplet self-spin motion.

physics.flu-dyn↗

arXiv2021

The air-water interface of condensed water microdroplets does not produce H$_2$O$_2$

Recent reports on the production of hydrogen peroxide (H$_2$O$_2$) on the surface of condensed water microdroplets without the addition of catalysts or additives have sparked significant interest. The underlying mechanism is speculated to be ultrahigh electric fields at the air-water interface; smaller droplets present higher interfacial area and produce higher (detectable) H$_2$O$_2$ yields. Herein, we present an alternative explanation for these experimental observations. We compare H$_2$O$_2$ production in water microdroplets condensed from vapor produced via (i) heating water to 50-70 °C and (ii) ultrasonic humidification (as exploited in the original report). Water microdroplets condensed after heating do not show any enhancement in the H$_2$O$_2$ level in comparison to the bulk water, regardless of droplet size or the substrate wettability. In contrast, those condensed after ultrasonic humidification produce significantly higher H$_2$O$_2$ quantities. We conclude that the ultrasonication of water contributes to the H$_2$O$_2$ production, not droplet interfacial effects.

cond-mat.soft↗

arXiv2021

WIMP Dark Matter Hidden behind its Companion

The WIMP dark matter (DM) hypothesis now is in an awkward position, owing to the stronger and stronger exclusion from DM direct detection. In this article we design a mechanism to evade this constraint.The idea is simple. DM has a companion, and they are both charged under the DM protecting symmetry G; they admit the trilinear coupling DM-DM-companion, so the latter provides a portal to the standard model (SM) via, for instance, the coupling to Higgs doublet.Then, DM semi-annihilates into the companion to arrive correct relic density, without leaving DM-nucleon scattering signal. The idea can be realized for ZN symmetric models with N >2.We stress that this mechanism has the characteristics of co-annihilation, and as a matter of fact its effect becomes necessary near or above the TeV region. This means that it may be difficult to detect our dark matter directly or indirectly.

hep-ph↗

arXiv2021

Hi-Phy: A Benchmark for Hierarchical Physical Reasoning

Reasoning about the behaviour of physical objects is a key capability of agents operating in physical worlds. Humans are very experienced in physical reasoning while it remains a major challenge for AI. To facilitate research addressing this problem, several benchmarks have been proposed recently. However, these benchmarks do not enable us to measure an agent's granular physical reasoning capabilities when solving a complex reasoning task. In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities. Inspired by how humans acquire these capabilities, we propose a general hierarchy of physical reasoning capabilities with increasing complexity. Our benchmark tests capabilities according to this hierarchy through generated physical reasoning tasks in the video game Angry Birds. This benchmark enables us to conduct a comprehensive agent evaluation by measuring the agent's granular physical reasoning capabilities. We conduct an evaluation with human players, learning agents, and heuristic agents and determine their capabilities. Our evaluation shows that learning agents, with good local generalization ability, still struggle to learn the underlying physical reasoning capabilities and perform worse than current state-of-the-art heuristic agents and humans. We believe that this benchmark will encourage researchers to develop intelligent agents with advanced, human-like physical reasoning capabilities. URL: https://github.com/Cheng-Xue/Hi-Phy

cs.AI↗

arXiv2021

Efficient Byzantine-Resilient Stochastic Gradient Desce

Distributed Learning often suffers from Byzantine failures, and there have been a number of works studying the problem of distributed stochastic optimization under Byzantine failures, where only a portion of workers, instead of all the workers in a distributed learning system, compute stochastic gradients at each iteration. These methods, albeit workable under Byzantine failures, have the shortcomings of either a sub-optimal convergence rate or high computation cost. To this end, we propose a new Byzantine-resilient stochastic gradient descent algorithm (BrSGD for short) which is provably robust against Byzantine failures. BrSGD obtains the optimal statistical performance and efficient computation simultaneously. In particular, BrSGD can achieve an order-optimal statistical error rate for strongly convex loss functions. The computation complexity of BrSGD is O(md), where d is the model dimension and m is the number of machines. Experimental results show that BrSGD can obtain competitive results compared with non-Byzantine machines in terms of effectiveness and convergence.

cs.DC↗

arXiv2021

GIPA: General Information Propagation Algorithm for Graph Learning

Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation. In this paper, we present a new graph attention neural network, namely GIPA, for attributed graph data learning. GIPA consists of three key components: attention, feature propagation and aggregation. Specifically, the attention component introduces a new multi-layer perceptron based multi-head to generate better non-linear feature mapping and representation than conventional implementations such as dot-product. The propagation component considers not only node features but also edge features, which differs from existing GNNs that merely consider node features. The aggregation component uses a residual connection to generate the final embedding. We evaluate the performance of GIPA using the Open Graph Benchmark proteins (ogbn-proteins for short) dataset. The experimental results reveal that GIPA can beat the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average test ROC-AUC of $0.8700\pm 0.0010$ and outperforms all the previous methods listed in the ogbn-proteins leaderboard.

cs.LG↗

arXiv2021

Metallic nature and site-selective magnetic collapse in iron oxide Fe4O5 at the extreme conditions of Earth's deep interior

Properties of iron oxides at the extreme conditions are of essential importance in condensed matter physics and Geophysics. The recent discovery of a new type of iron oxide, Fe4O5, at high pressure and high temperature of Earth's deep interior attracts great interests. In this paper, we report the electronic structure and the magnetic properties of Fe4O5 predicted by the density functional theory plus dynamic mean field theory (DFT+DMFT) approach. We find that Fe4O5 stays metallic from ambient pressure to high pressure. The magnetic moments of iron atoms at the three different crystallographic positions of Fe4O5 undergo position-dependent collapse as being compressed. Such site-selective magnetic moment collapse originates from the shift of energy levels and the consequent charge transfer among the Fe-3d orbits under compression.

cond-mat.str-el↗